In recent years, there has been an enormous increase in the amount of digital media available online. Services, such as Apple's iTunes® for example, enable users to legally purchase and download music. Other services, such as Yahoo!® Music Unlimited and RealNetwork's Rhapsody® for example, provide access to millions of songs for a monthly subscription fee. YouTube® provides users access to video media. As a result, media items have become much more accessible to consumers worldwide. However, the increased accessibility of media has only heightened a long-standing problem for the media industry, which is namely the issue of linking users with media that matches their preferences.
Many companies, technologies, and approaches have emerged to address this issue. Being able to link users with media that match their preferences allows companies to effectively make recommendations of media items to users. Some companies assign ratings to attributes of identified media. The ratings are assembled to create a holistic classification for the media that is then used by a recommendation engine to produce recommendations. Other companies take a communal approach wherein recommendations are based on the collective wisdom of a group of users with similar tastes by profiling the habits of a particular user based on the information provided by the user and then searching similar profiles of other users. Either approach involves the soliciting, assembling and reviewing of information about a user and/or the user's media likes or dislikes. That information then is used to establish user preferences on which to base media recommendations.
In some recommendation generation schemes, the user's media preferences are used to determine recommendations. User preferences allow more accurate targeting of recommendations. A user may establish his preferences by assigning a weight to different media categories. These media categories may include, for example, genre, artist, title, album or presentation, date of release, or the like. The weight assigned by the user for each of the media categories is used to define the user's preferences, and from those preferences, a profile for that user. One example of such an approach is described in U.S. patent application Ser. No. 11/484,130, entitled “P2P NETWORK FOR PROVIDING REAL TIME MEDIA RECOMMENDATIONS,” filed on Jul. 11, 2006, which is hereby incorporated herein by reference in its entirety.
Media categories may however contain a large number of fields. To effectively assign a weight to a media category, the user must assign a weight to each of the fields within that media category. This may be a difficult and time consuming effort for the user depending on the number of fields in a media category. The genre media category provides a pertinent example of this problem.
Genre may be considered the predominant media category for determining a user's preferences. The genre category is generally recognized as comprising up to one hundred and forty-eight (148) different fields. Users may not spend, and in most cases, will not spend the time to assign 148 different weights to these fields. Alternatively, the user may opt to just assign weights to only certain selected fields of interest. In either scenario, the weighting of the genre media category would be incomplete. As a result, preferences calculated using the incomplete weighting of a media category would be inherently inaccurate. Therefore, any media item recommendation based on those preferences would be inaccurate. Accordingly, there is a need for a system and method to effectively assign preference weights to a set of fields within a media category, and particularly the genre media category, without the user having to individually assign a weight to each field within that media category.